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Weighted Probabilistic Opinion Pooling Based on Cross-Entropy
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SYSNO ASEP 0450905 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Weighted Probabilistic Opinion Pooling Based on Cross-Entropy Author(s) Sečkárová, Vladimíra (UTIA-B) RID Number of authors 1 Source Title Neural Information Processing : 22nd International Conference, ICONIP 2015. - Cham : Springer International Publishing, 2015 / Sabri A. ; Tingwen H. ; Weng K.L. ; Qingshan L. - ISSN 0302-9743 - ISBN 978-3-319-26534-6 Pages s. 623-629 Number of pages 7 s. Publication form Online - E Action 22nd International Conference on Neural Information Processing (ICONIP2015) Event date 09.11.2015-12.11.2015 VEvent location Istanbul Country TR - Turkey Event type EUR Language eng - English Country CH - Switzerland Keywords Minimum cross-entropy principle ; Kullback-Leibler divergence ; Linear opinion pooling ; Combining probability distributions Subject RIV BC - Control Systems Theory R&D Projects GA13-13502S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000371579600071 EID SCOPUS 84951804385 DOI 10.1007/978-3-319-26535-3 Annotation In this work we focus on opinion pooling in the finite group of sources introduced in [Seckarova, 2015]. This approach, heavily exploiting Kullback-Leibler divergence (also known as cross-entropy), allows us to combine sources’ opinions given in probabilistic form, i.e. represented by the probability mass function (pmf). However, this approach assumes that sources are equally reliable with no preferences on, e.g., importance of a particular source. The discussion about the influence of the combination by preferences among sources (represented by weights) and numerical demonstration of the derived theory on an illustrative example form the core of this contribution. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2016
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